How well a system generalizes between different datasets (train on A, test on B) is a key measure of computer vision object detection algorithms. Generalization was identified as a critical shortcoming of support vector machine (SVM) algorithms [1]}. The size and complexity of image datasets has increased significantly over the past decade. In the case of automotive applications the development of region proposal networks (RPN) has extended the applications from databases containing images of single objects (e.g., CalTech 101 and ImageNet) to images representing more complex scenes (BDD, KITTI, Cityscape). Quantifying generalization of neural networks between these complex image datasets remains an important question [2]}. Understanding generalization can not only help us understanding the limit of the deep neural network, but also guide us feeding more suitable data to it.
